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Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey

by Waseem Abbas *,† and David Masip Rodo
Multimedia and Telecommunications Department, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(15), 3274; https://doi.org/10.3390/s19153274
Received: 31 May 2019 / Revised: 19 July 2019 / Accepted: 21 July 2019 / Published: 25 July 2019
(This article belongs to the Section Intelligent Sensors)
Neuroscience has traditionally relied on manually observing laboratory animals in controlled environments. Researchers usually record animals behaving freely or in a restrained manner and then annotate the data manually. The manual annotation is not desirable for three reasons; (i) it is time-consuming, (ii) it is prone to human errors, and (iii) no two human annotators will 100% agree on annotation, therefore, it is not reproducible. Consequently, automated annotation for such data has gained traction because it is efficient and replicable. Usually, the automatic annotation of neuroscience data relies on computer vision and machine learning techniques. In this article, we have covered most of the approaches taken by researchers for locomotion and gesture tracking of specific laboratory animals, i.e. rodents. We have divided these papers into categories based upon the hardware they use and the software approach they take. We have also summarized their strengths and weaknesses. View Full-Text
Keywords: locomotion tracking; gesture tracking; behavioral phenotyping; automated annotation; neuroscience; machine learning locomotion tracking; gesture tracking; behavioral phenotyping; automated annotation; neuroscience; machine learning
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Abbas, W.; Masip Rodo, D. Computer Methods for Automatic Locomotion and Gesture Tracking in Mice and Small Animals for Neuroscience Applications: A Survey. Sensors 2019, 19, 3274.

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